Information Science and Technology
Mathematical Informatics
String Algorithms and Data Structures Laboratory
Members : Prof. Shunsuke Inenaga / Assoc.Prof. Yuto Nakashima
keywords : Algorithms, Data structures, Data Compression, Combinatorics on Words, Theoretical Computer Science
Strings are concepts that generalize sequential data such as text, time series, labeled trees/graphs, and 2D arrays. Our research interests are in designing fast and space-efficient algorithms for processing strings, with particular emphasis on their theoretical perspectives. Since the discovery of classical algorithms including KMP, suffix trees, and LZ compression, string algorithmics has been one of the most important sub-fields in theoretical computer science. Also in the real world, string algorithms are commonly utilized as core building blocks of information retrieval systems and data compression programs. Further, string-related problems are commonly seen in competitive programming contests. Our approach for tackling massive sequential data is first to reveal mathematical properties of strings using the theory of “word combinatorics”, and then to develop advanced “algorithms and data structures” techniques.
Mathematical Engineering Laboratory
Members : Prof. Jun’ichi Takeuchi / Prof. Yutaka Jitsumatsu / Assoc.Prof. Tesshu Hanaka / Asst.Prof. Yoshinari Takeishi
keywords : Machine Learning, Minimum Description Length (MDL) Principle, Stochastic Complexity, Information Geometry, Information Theory
Our laboratory aims to find out mathematical structures of various problems in computer science and digital communication and to derive universal solutions based on the mathematical structures. Our research topics include basic theories such as learning theory, machine learning, information theory, information geometry, communication theory, network theory, nonlinear system theory and their applications. Specific applications are cyber-attack detection on the Internet, super resolution, pattern recognition, CDMA communication, analog/digital conversion, and error correcting codes. Through these researches, we develop human resources who are responsible for the fundamental technologies in advanced information society in future.
Algorithm Theory Laboratory
Members : Prof. Yukiko Yamauchi
keywords : Algorithms, distributed systems, distributed coordination of mobile robots
Algorithm Theory Laboratory is widely interested in the principles of computing, particularly algorithm theory. For a bunch of problems originating from the real world or motivated by theoretical computer science, our research interest includes design of algorithms and mathematical analysis from the view point of correctness, efficiency, robustness, etc. Prof. Yamauchi is mainly involved in a variety of topics on distributed coordination, such as fault-tolerance of distributed systems, self-organization of autonomous mobile robots, and game theory in distributed environment.
Information Security & Multimedia Security Laboratory
Members : Prof. Kouichi Sakurai / Asst.Prof. Wissam Razouk
keywords : Network Security, Security Camera, Security Robot, Adversarial Machine Learning, Computer Security, Cryptography
Nowadays, not only people but things are getting increasingly interconnected in what is called Internet of Things (IoT). In a world where almost everything is connected, if an attacker gets control of one of these networks it can be disastrous. Attacks can go as far as changing the election results of a country (USA’s 2016 election was strongly influenced by Russian cyberattacks). Moreover, in a recent report from Forbes, cybercrime is projected to reach 2 trillion dollars by 2019. To protect society, we research new technologies and paradigms for security related applications.
Laboratory of Intelligent Systems
Members : Assoc.Prof. Danilo Vasconcellos Vargas
keywords : Deep Learning, Neuroevolution, Action/Image Recognition, Multi-agent based Intelligence, Bioinspired Artificial Intelligence, Artificial General Intelligence, Evolutionary Computation, Reinforcement Learning, Adversarial Machine Learning
In the Laboratory of Intelligent Systems we create novel AI engines as well as build robust and adaptive intelligent systems. Current AIs can solve 19×19 versions of Go but behave poorly on easier 9×9 versions of the same game. Similarly, image recognition algorithms can reach 96% accuracy (supra-human) on tests and be fooled by only one pixel change. In other words, current AI lacks the robustness and adaptation present in even simple living beings. AI is based on engines that allows it to learn and reason over things, this lab builds novel engines based on different paradigms to reach high levels of robustness and adaptiveness intrinsically. Interestingly, by increasing the robustness and adaptiveness, other problems like Transfer Learning, One-Shot Learning would also be solved at the same time, igniting, possibly, a new age of intelligent systems.
Quantum Information Lab
Members : Assoc. Prof. Hiroyasu Tajima
keywords : quantum information theory, resource theory, symmetry and its breaking, quantum thermodynamics, measurement theory
Our laboratory conducts research at the interface between information and physics. Although information theory and physics may appear to address very different subjects, they are in fact deeply interconnected. We focus particularly on the relationship between quantum information theory and nonequilibrium statistical physics, combining the conceptual frameworks and mathematical techniques of both fields to analyze a wide range of phenomena.
Our primary interests lie in two broad questions: the fundamental limits inherent in physical processes, such as thermodynamic transformations, measurement processes, and computation; and the extent to which quantum effects can modify or overcome these limits. Because these themes span both foundational and applied aspects of physics, our group actively explores both directions.
On the foundational side, we investigate topics such as universal constraints on quantum dynamics arising from symmetry. On the applied side, we study quantum-mechanical mechanisms for realizing heat engines that surpass classical power bounds while approaching Carnot efficiency. Through these efforts, our laboratory aims to deepen the theoretical understanding of quantum-enhanced physical processes and to contribute to future quantum technologies.
Intelligence Science
Cognitive Science Laboratory
Members : Prof. Shuji Mori / Prof. Kazunori Shidoji
The cognitive science laboratory explores functions of human mind for their engineering applications. Prof. Mori investigates auditory temporal resolution and attempts to develop new hearing tests, using a variety of psychophysical techniques. Prof. Shidoji focuses on estimation of driver’s state in driving simulator and real-road driving, its application to development of automated driving system and driver support system, and perception and cognition in virtual reality environment.
Data Mining Laboratory
Members : Prof. Einoshin Suzuki / Asst.Prof. Tetsu Matsukawa
keywords : Data mining, Machine learning, Autonomous mobile robot, Robot, Deep learning, Anomaly detection, Exception discovery, Classification
In data mining, which aims at sophisticated discovery of potentially useful and understandable patterns from massive data, we tackle diverse issues from fundamental ones to applications with various bases including machine learning. Examples include data processing such as data squashing and data structure, pattern discovery such as various types of exceptions and rules, pattern interpretation such as information visualization and human factors, and other issues such as problem formalization. Moreover we conduct various kinds of research including autonomous mobile robots using machine learning and data mining techniques as well as deep learning on image, video, and text data.
Machine Learning Theory Laboratory
Members : Prof. Eiji Takimoto / Prof. Kohei Hatano
keywords : Online decision making, Computational learning theory, Computational compelxity
The problem of decision-making by predicting future data from the past arise in many applications such as stock investment, item recommendation, routing, updating kana-kanji conversion dictionary, and so on. Our group is trying to develop ingenious methods of decision-making for various problems by using machine learning techniques. On the other hand, we also apply the methods developed to optimization problems in machine learning. Furthermore, for various classes for knowledge representation such as Boolean circuits, decision diagrams, neural networks, comparator networks, we investigate their mathematical properties and relationships between them, thereby we analyze computational efficiency of decision making methods.
Multi-Agent Laboratory
Members : Prof. Makoto Yokoo / Prof. Yuko Sakurai / Assoc.Prof. Taiki Todo / Asst.Prof. Miyuki Koshimura
keywords : Market Design, Artificial Intelligence, Mechanism Design, Matching, Combinatorial Auctions, Repeated Games, Prisoner’s Dilemmas, Hospitals/Residents Matching, POMDP, Constraint Satisfaction
The main research field in our laboratory is multi-agent systems, where multiple intelligent agents coexists. Especially, our research focuses on systems where humans and software agents interact and coordinate. Specific research topics include two-sided matching and auctions, for which we model agents’ behaviors based on game theory and micro-economics, and develop/analyze social decision rules based on algorithm theory and optimization.
Neuroimaging and Neuroinformatics Laboratory
Members : Prof. Keiji Iramina
keywords : Neuroscience, Neuroengineering, Brain Information Science, Event related potential, fMRI, Brain Machine Interface, Cognitive function, Mild cognitive impairment (MCI), Alzheimer disease
Iramina’s lab is under the administration of the Faculty of Information Science & Electrical Engineering, Kyushu University, and Graduate School of Systems Life Sciences which is a unique educational organization.
There are two major research fields in our lab. One is brain function imaging which aims at the elucidation of human brain function; the other one is brain function modeling which is applied to various fields by constructing the model of brain activation. In details, we study in the fields of the measurements of brain function by EEG (Electroencephalography), NIRS (Near-Infrared Spectroscope) and TMS (Transcranial Magnetic Stimulation), the development of measurement technology and the simulation of brain activation. The elucidation of the mechanism of brain function is one of foundations of life science, and it can be applied to almost all the fields. Have a deep understanding of brain information processing, and apply the research results to fields of life science, medicine, welfare and education is the purpose of our study.
Since we are studying in an interdisciplinary domain, we take into account the collaboration of medicine, biology, pedagogy and psychology is important in our study.
Natural Language Processing Laboratory
Members : Prof. Yoichi Tomiura
keywords : Large Language Model, Deep Learning, Natural Language Processing
Natural Language Processing is a field of research that focuses on the technology of processing text written in natural languages such as Japanese and English using computers. Representative applications include Kana-Kanji Conversion, Machine Translation, Sentiment Analysis, and Question-Answering systems. Chat systems based on Large Language Models such as ChatGPT and Gemini have emerged, and their performance has improved dramatically, potentially transforming social life. However, the underlying mechanism behind the advanced processing capabilities of Large Language Models remains unclear. In our laboratory, we are working to elucidate the mechanisms of Large Language Models and improve their performance. Additionally, we are conducting research on applications such as the search assistance and the extraction of material properties from academic papers using Large Language Models.
3D Multimedia Contents Laboratory
Members : Prof. Yoshihiro Okada
keywords : 3D-CG, Multimedia, HCI
Our laboratory is researching and developing fundamental technology for 3D multimedia contents of still images, videos, 3D shapes, motion data and so on. In addition to search and creation technology for them and visualization technology, the research interests of our laboratory also include voice input/output interface for 3D-CG contents, motion input interface based on video images, virtual reality applications using a haptic device like Phantom, network collaboration technology for instantly and easily creating a virtual space of 3D-CG in which multiple users can take various intellectual activities collaboratively with each other. Our laboratory also conducts research on the development environments of 3D games and educational materials using recent ICT.
e-Science Laboratory
Members : Assoc.Prof. Daisuke Ikeda
keywords : computational complexity, mathematical logic, foundation of mathematics, numerical analysis, validated numerics, differential equations, computation model, randomness
Due to big data and the development of ICT, computer simulation and data analysis with computers are used in many disciplines. While computers have been supporting tools for experts, there is an emergence of a new discipline, called e-Science, in which computers are main approaches.
In our lab, under the vision that “general public will be participating a process of science”, we are conducting researches, such as computer simulation and data mining, and infrastructures for e-Science.
Statistical Learning Laboratory
Members : Assoc.Prof. Hiroto Saigo
keywords : bioinformatics, cheminformatics, machine learning, statistics, data mining
Our primary research interests is in the development of statistical learning methods, which gives foundation for data science and deep learning. Due to the recent public interests in artificial intelligence and machine learning, various industries are seeking a way to make good use of it. In solving real-world problems, however, what is required for data scientists is not only to have deep understanding on various machine learning methods, but also to become familiar with the domain of the facing problem. In this regard, we put an emphasis on dealing with real-world data, and always use it for evaluating proposing methods.
One of the characteristics of this group is its focus on biology and chemistry, such as developing methods for handling genes and chemical compounds, however, our research interests is not limited to these areas.
Intelligence and Cultural Evolution Laboratory
Members : Assoc.Prof. Eita Nakamura
keywords : Intelligent informatics for artistic culture, cultural evolution science, music information processing, machine learning, evolutionary theory, interdisciplinary physics
Our laboratory conducts research on the mechanisms of intelligence and evolutionary phenomena that support artistic culture and on information technology that contributes to cultural development. With a goal of constructing a unified model describing intellectual activities of creators and audiences as well as the evolution of knowledge distribution within social groups, our research topics range from fundamental studies on machine learning, data generation models, evolutionary theory, interdisciplinary physics, etc. to information processing techniques of musical, visual, and literary arts. We foster scientists and engineers who can research and develop artificial intelligence technologies from the perspective of social development, based on their expertise in informatics and physics, including advanced machine learning and mathematical theories of evolution.
Lab for Sensory Information Processing
Members : Prof. Willy Wong
keywords : sensory information, neural models, AI in medicine, algorithms, brain activity
We utilize various methods to improve the hearing and vision of individuals with impairments and those who are aging. Through experiments and by leveraging algorithms, machine learning, and AI, we are solving complex health problems. Additionally, we focus on how sensory neurons transmit information and aim to elucidate the mechanisms behind this process. Based on these research findings, we are working to gain a deeper understanding of auditory processing and to design innovative devices for people without vision.
Advanced Information and Communication Technology
Cyber-Physical Computing Laboratory
Members : Prof. Koji Inoue / Prof. Masao Hirokawa / Assoc.Prof. Yusuke Matsunaga / Assoc.Prof. Takatsugu Ono / Assoc.Prof. Teruo Tanimoto / Assoc.Prof. Olivia Chen / Assoc.Prof. Ilkwon Byun / Asst.Prof. Yuting Zhao / Asst.Prof. Takeru Hidaka
keywords : Hardware security, Data center, Warehouse-scale computing, High-performance computing, Architecture, Cyber-physical system
Our research goal is to explore next-generation computer system architecture that can be achieved by integrating the information and electrical/electronic technologies. We also aim to develop new applications that stand on growing computing performance in order to solve critical issues in the world such as energy issue, cyber-security, and so on. Our scope is from emerging devices such as single-flux-quantum and nanophotonics to computer architecture, system software, and applications.
Wireless Communication Laboratory
Members : Prof. Osamu Muta / Asst.Prof. Ahmad Gendia
keywords : Wireless Communications, Cellular phone, Wireless LAN, Wireless sensing, Wireless IoT
To deal with the rapid increase of mobile data traffic in wireless communications, it is required to develop wireless communication techniques that achieve high spectrum efficiency. In addition, in the next generation mobile communications, the integration of wireless sensing technologies with wireless communications is important for the advancement of future wireless cellular networks. In our laboratory, we are doing research on next-generation wireless communication techniques and their applications.
High-Performance Computing Laboratory
Members : Assoc.Prof. Satoshi Ohshima / Assoc.Prof. Takeshi Nanri
keywords : Parallel computing, GPU, Supercomputer
To extract the maximum performance from the cutting-edge high-performance computing hardware, it is essential to have new software technologies that can appropriately utilize them.
In our High-Performance Computing Laboratory, we analyze the characteristics of the hardware technologies used in a wide range of computer systems, including supercomputers. Furthermore, we research and develop new algorithms and programming techniques to bring out the maximum performance of the entire system and contribute to the enhancement of software performance by globally disseminating the results of our research, in forms such as libraries. Moreover, through these research activities, we strive to cultivate researchers and technicians who will become experts in computer performance, active in various fields.
Real World Robotics
Laboratory for Image and Media Understanding
Members : Prof. Atsushi Shimada / Assoc.Prof. Fumiya Okubo / Asst.Prof. Cheng Tang / Asst.Prof. Gen Li
keywords : Learning Analytics, Image & Media Processing, Pattern Recognition, Artificial Intelligence, Explainable AI
In the Laboratory for Image and Media Understanding (LIMU), our goal is to establish a novel framework to (1) retrieve social information from observation data obtained with various sensors and (2) to create innovative content for the society by analyzing those data. While developing the tools necessary to build such a framework, we carefully design the algorithms so that anybody in the society can later interact with the cyber–physical world to improve analysis performances and users experience. In our research on video analysis techniques, we are developing fundamental techniques for understanding videos acquired from cameras, such as methods for detecting objects in the observation area and for detecting abnormal events. On the other hand, we also conduct analysis of educational big data such as students’ learning activities collected from digital textbook systems and learning management systems. The various educational data are analyzed to provide real-time feedback systems for visualizing student’s learning activities and teaching materials recommendation systems personalized for students individually. These results can be used to develop services leading to a more efficient and sophisticated society. Furthermore, in order to apply to new fields such as educational big data, we are also conducting research on the theory on models of computation for various phenomena in the nature and society.
Laboratory for Real-world Informative Robotics
Members : Prof. Ryo Kurazume / Assoc.Prof. Akihiro Kawamura / Asst.Prof. Shoko Miyauchi / Asst.Prof. Kohei Matsumoto / Asst.Prof. Tomoya Itsuka
keywords : Service robots, Sports engineering, Soft robotics, Medical image processing, Childcare assistance technology, Intelligent robotics
We conduct research on fundamental technologies for real-world robotics to enable safe symbiosis between humans and robots and to realize a more convenient and enriched society. In recent years, robots have become increasingly visible in offices and public spaces. However, there remain significant challenges, such as operating in unstructured environments, handling complex tasks, and ensuring safety.
To address these challenges, our research focuses on a wide range of technologies, including software for complex task execution, flexible and safe hardware, sensors for acquiring diverse types of information, environmental informationally structuring to enable intelligent environments, and artificial intelligence for advanced perception and action generation.
In addition, we actively explore the application of these core technologies beyond robotics fields, extending into areas such as sports, caregiving, healthcare, and childcare. Through our technologies, we aim to contribute to improving the quality of life for all.
Human Interface / Real Data Analysis Laboratory
Members : Prof. Seiichi Uchida / Prof. Ryoma Bise / Assoc.Prof. Brian Kenji Iwana / Assoc.Prof. Daiki Suehiro / Asst.Prof. Katsutoshi Masai / Asst.Prof. Shoji Toyota / Asst.Prof. Shota Harada
keywords : Artificial intelligence, Deep learning, Neural network, Medical image, Biosignal, Time series, Document and character analysis
Pattern recognition is a research field that focuses on the artificial realization of the human cognitive system. It is still difficult even though computers are highly developed today. For example, we humans can easily recognize a car at a glance as “That is a car.” However, there are numerous models in cars, and appearance will change depending on a point of view even if we look at the same model. The easiest way to handle this issue is to classify the input based on the similarity to patterns stored in a computer in advance, but challenges remain such as the definition of similarity. The key point is how to handle variety in patterns that causes difficulty. In this laboratory, we develop pattern recognition techniques and the related applications such as image processing/recognition, bioimage informatics, machine learning, and character engineering/science. We are challenging these attractive problems with our unique techniques and competing against the world.
Computer Vision, Graphics & VR Laboratory
Members : Prof. Hiroshi Kawasaki / Assoc.Prof. Thomas Diego / Asst.Prof. Takafumi Iwaguchi
keywords : 3D Human Body Measurement from RGB-D Cameras, 3D Shape Reconstruction of Extreme environment, Analysis of In-Vehicle Camera Images for Autonomous Driving, Medical Image Analysis, Computational photography and material analysis
In this laboratory, we focus on computer vision (CV) and computer graphics (CG) research as well as application to virtual and augmented reality systems (VR/AR). To contribute to those research areas, efficient acquisition, modeling and photo-realistic visualization techniques are the core. For example, we are working on 3D scene reconstruction using color and depth (RGB-D) cameras, called the RGB-D Simultaneous Localisation and Mapping (SLAM). Part of our research focus on the reconstruction of the dynamic human body using RGB-D cameras. In this project, we reconstruct 3D models from a single image using Convolutional Neural Networks (CNN). Another research project is light transport analysis based on computational photography, an imaging method that combines an optical system and a computer. By using the outcomes of those researches, development of medical imaging systems and intelligent transportation systems is also our important mission.
Human Data Interaction Laboratory
Members : Prof. Shin’ichi KONOMI / Asst.Prof. Yuta TANIGUCHI
keywords : Human-Computer Interaction, Data Mining, Sensing, Learning Analytics, Ubiquitous Computing
Our research topics include design, methods and techniques for making interactions between humans and data more effective, thereby making it easier to address societal issues based on a large amount of data. We work on different sensing methods including crowd sensing; different data analysis and visualization techniques based on data mining; and applied research of data analytics. In particular, we actively pursue applied research on learning analytics to improve learning and teaching based on data. We also conduct research on ubiquitous computing for the support of collaboration and problem solving.
Artifical Intelligence in Education Laboratory
Members : Prof.Chengjiu Yin
keywords : Learning Analytics, Sensor-enhanced educational environments, Educational environments using AR/VR
The Artificial Intelligence in Education (AIE) Lab conducts research and development to realize advanced educational environments by leveraging the power of big data and AI. We research on various educational environments including formal education, agriculture, medicine, healthcare, and information with universities in Japan and overseas.
We are researching the infrastructure for processing big data collected from learning systems, course registration systems, and various sensors, as well as its application to improve learning and education. We welcome students who are interested in educational support using AI and ICT technologies.
Advanced Software Engineering
SocialTech Laboratory
Members : Assoc.Prof. Ashir Ahmed
keywords : Digital Health, LLM, Medical AI, Community Healthcare
SocialTech Lab conducts research and development of innovative technologies aimed at solving social issues, particularly in the field of healthcare. The lab focuses on designing healthcare systems that can deliver high-quality medical services equitably, even in resource-limited or remote areas. To achieve this, we promote the development and social implementation of digital health technologies that emphasize accessibility, affordability, and personalization.
At the core of our work is the Portable Health Clinic (PHC)—a mobile telemedicine system designed to provide basic healthcare in low-resource settings. It also serves as a research platform for validating and refining new digital healthcare technologies in real-world conditions.
Our research themes include high-precision health data collection, digitization of analog medical records using OCR and computer vision, and structuring clinical conversations and audio data through AI and natural language processing (NLP). By integrating these technologies, we aim to enhance clinical decision-making, enable personalized care, and develop predictive models for disease prevention.
SocialTech Lab combines scientific knowledge with field-based practice to contribute to the creation of next-generation, sustainable, and inclusive healthcare systems.
Software Engineering Laboratory
Members : Prof. Yasutaka Kamei / Asst.Prof. Masanari Kondo / Project Asst.Prof. Tao Xiao
keywords : Software Engineering, Automated Program Generation and Repair, Generative AI and LLMs, Open Source
We now live in an era where software is embedded in nearly every product, from smartphones to automobiles. Software engineering is an academic field that studies methods for developing such software efficiently and with high quality. Our laboratory focuses on research that combines software engineering with artificial intelligence (AI). For example, we are developing AI technologies that can automatically generate programs or detect and fix bugs. We are also working on techniques to efficiently develop AI-based systems themselves. For instance, we are researching software technologies that can automatically identify and fix the causes of incorrect behavior in AI systems.
Intelligent Software Engineering Laboratory
Members : Prof. Jianjun Zhao / Asst.Prof. Yaokai Feng / Asst.Prof. Zhenya Zhang
keywords : Intelligent Software Engineering, Software Testing, Deep Learning, Program Analysis and Verification, Programming Language, Artificial Intelligence, Automatic Programming
Software engineering (SE) is the systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software. Artificial intelligence (AI) is a study on the design and realization of an intelligent information processing system by computer. The intelligent software engineering laboratory aims to construct reliable and secure software systems and AI systems by synergizing software engineering with artificial intelligence. Specifically, we are doing research with three directions.
- Software engineering for AI: We are developing methods to deeply understand defects (bugs) and adversarial examples in artificial intelligence (deep learning) systems, and approaches (analysis, testing, debugging, and verification) to guarantee the reliability and security of artificial intelligence (deep learning) systems.
- Software Automation: We are developing approaches for automatic code generation and bug fixing of software systems using artificial intelligence (deep learning).
- Intelligent IDE: We are building intelligent software development environments.
HumanoPhilic Systems Laboratory
Members : Prof. Yutaka Arakawa / Asst.Prof. Yugo Nakamura
keywords : IoT, Activity Recognition, Behavior Change Support System, Wearable Computing, Learning Analytics, Energy Harvesting, Stress Estimation Work Engagement Estimation, Ubiquitous Computing, Pervasive Computing, Mobile Computing, Web Information System, Disaster Information System, Notification Management, Social Data Analysis, Participatory Sensinc, Vehicular Sensing, Cyber Physical System, Sensor Network, Application
HumanoPhilic Systems Laboratory conducts research on cyber-physical systems (CPS: Cyber-Physical Systems) that support human life, by combining various information technologies, such as sensing from the real world, data processing in the cloud, and networking that conncts them. The term “HumanoPhilic” is the combination of “human” and “philic” which means having a high affinity.
We focus on human activity recognition using sensors (IoT) and machine learning (AI). Our research topics include both hardware development and software implementation. A major research issue is to explore what kind of sensors and algorithms can recognize the internal sate (Emotions and stresses) as well as the external state of a person (physical action). Furthermore, in recent years, as novel research beyond human activity recognition, we started focusing on a behavior change support system (BCSS). BCSS means information technologies that affect human future behavior.
Human-centered Intelligence Laboratory
Members : Assoc.Prof. Tsunenori Mine
keywords : Data Mining, Text Mining, Information Sharing, Information Recommendation, Personalization, Machine Learning, Multi-Agent Systems
We aim to study human-centered intelligence. To this end, we analyze real data under real situations and develop mechanisms to estimate, extract, and generate information users want and provide it to them when they need, considering their contexts, intentions, preferences, interests, and privacy issues. The projects we are conducting are roughly divided into four: 1) Text Mining and Message Generation, 2) Data Mining for Intelligent Transport Systems (ICT), 3) Educational Data Mining (EDM), and 4) Multi-modal Data Mining and Information Recommendation. For 1), we develop dialogue systems (Chat-bots) which automatically answer user queries, discriminating out-of-domain or out-of-intent queries with query augmentation techniques; we estimate user emotions; we study named entity recognition from patent documents and research papers, etc. For 2), we estimate city bus travel time, arrival time, and delay time, abnormal driving behaviors, and road situations by analyzing multi-modal ICT-related data such as vehicle probe data, obtained from ICT-devices (ETC 2.0 devices), dashboard camera data, weather-related data, traffic and human stream data, etc. For 3), we develop methods to estimate student learning situations and performance, to give automatic feedback analyzing student data such as student self-reflective comments freely-written after each lesson, e-learning logs etc., and to automatically score student short answers. Finally, for 4), we estimate recommended handcrafted works, which work will be bought in certain period of time, and who created the works, and track trends or changes of the works; we also estimate useful product review documents, and develop new collaborative filtering algorithms using Graph Convolutional Networks to extract useful information from user-item interactions. We have been having a joint operation system with the Humanophilic Systems Laboratory since 2020, which conducts
research on cyber-physical systems being close to people, and been focusing on acquisition methods of various data.
Behavior Informatics Laboratory
Members : Assoc.Prof. Masaya Okada
keywords : Behavior Informatics, Situated Intelligence, Multimodal Sensing, Learning Analytics, Embodied Cognitive Science
Human beings have social intelligence emerging in the world. For example, they estimate the tacit meaning of surrounding situations, learn in and from the context of real-world situations, and generate behavior adapted to their situations. However, little is known about the generation mechanism of this type of social intelligence. We are developing ubiquitous and multimodal sensing technologies to understand and enhance the mechanism of this type of social intelligence.
Hacking Reality Laboratory
Members : Assoc.Prof. Shogo Fukushima
keywords : Virtual Reality, Human-Computer Interaction, Emotional Intelligence (EI), Human Memory Augmentation, TEL (Technology Enhanced Learning)
Virtual Reality (VR) is a technology that extracts the essence of the real world and reproduces it beyond the constraints of time and space. In our lab, we are not only focused on recreating this essence but also on reconstructing it from diverse perspectives and values to create new senses of reality. The word “Hack” embodies our desire to express and create new realities that transcend established concepts and everyday conventions.
Currently, we are particularly engaged in utilizing VR to transcend users from the physical constraints (such as weight and space) and conventional frameworks of the real world. Our research is divided into the following three units:
- Visual transcending: Displays using aerial imaging beyond the frame
- Emotional transcending : Emotion management methods based on understanding the interactions of neural control
- Body transcending : Promotion of skill and knowledge acquisition using virtual bodies
Advanced Distributed Processing Systems
Advanced Network and Cybersecurity Laboratory
Members : Prof. Koji Okamura
keywords : Internet, Malicious software analyzing , White Hacker, Cyber Range, SDN (Software Defined Network), Machine Learning
The main research topics in this laboratory is Advanced Internet and Cybersecurity. Various research themes on networking and security are ongoing with companies and international partners in the world.